Mohamad Firdaus Che Abdul Rani

Bio: Mohamad Firdaus Che Abdul Rani is an academic researcher from Asia Pacific University of Technology & Innovation. The author has contributed to research in topics: The Internet & Email address harvesting. The author has an hindex of 3, co-authored 10 publications receiving 20 citations. Previous affiliations of Mohamad Firdaus Che Abdul Rani include Universiti Teknologi MARA.

Web-based learning tool for primary school student with dyscalculia

Abstract: Web-based learning tool provides incorporated environment of various technologies to support diverse instructor and learner needs via the Internet. Electronic learning materials can be accessed easily by normal students but not for dyslexic students. This is because student with Dyslexia needs more attention and guidance from the instructor and special tool is needed to improve their learning environment. This paper proposed the design for a Math Web-based learning tool based on pedagogical techniques for dyscalculia.

Questionify: Gamification in Education

Abstract: In the education industry, lecturers are finding ways to improve students’ concentrations and grades by using smart devices to track students’ assignment or tutorial progress. One of the few possible and attractive solution is by using the gamification technique. This paper proposes an educational application called Questionify that implements the gamification elements and allow users to collect points, gain achievements, increase motivation and engagement towards students’ coursework in Software Engineering subject. Questionify is developed using C# and Java language has been evaluated using questionnaire among 24 respondents. The findings showed that the respondents believe that gamification can do better in education as compare to the traditional method of teaching the students. In the future, this gamification approach will be tested on more technical subjects such as programming and networking subjects to help students engage in a different learning approach.

Heartbeats: music recommendation system with fuzzy inference engine

Abstract: In developing a music recommendation system, there are several factors that can contribute to the inefficiency in music selection. One of the problems persists during the music listening is that common music playing application lacks the ability to acquire context of the user. Another problem that common music recommendation system fails to address the is emotional impact of the recommended song. To address this gap, this paper presents a music recommendation system based on fuzzy inference engine that considers user activities and emotion as part of the recommendation parameters. The system includes building a smart music recommendation system that has user profiling capabilities to recommend correct songs based on the user’s preferences, mood and time. Findings of the this paper have shown that Heartbeats’s fuzzy inference engine has successfully achieved its aim, which is to improve users’ music listening experience by giving suitable song recommendation based on user context situation.

Gamification concept for encouraging lecture attendance

Abstract: New generation of students has high dependence on technology and embrace social learning environments that have low barrier to access. This means in-class lectures are not popular anymore, and in fact has become a burden for them to cope. To resolve the issue of low student attendance, this paper proposes a character growth game with the concept of gamification in education that is able to track and reward student attendance called PetAttendToClass. PetAttendToClass is a client-based system developed using C# and unity3D game engine. Although finding from the UAT session revealed that some users believed that attendance is the responsibility of the student, gamification is meant to turn this mundane responsibility into something motivative, interactive and interesting. It is hoped that by gamifying the class attendance, student will be motivated to attend their daily classes.

EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls

Abstract: EEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns.

Pubudu: Deep Learning Based Screening And Intervention of Dyslexia, Dysgraphia And Dyscalculia

Abstract: Dyslexia, Dysgraphia and Dyscalculia are significant learning disabilities that affect around 10% of children in the world. Despite the advancement of technology literacy in the community, limited attention has been given for screening and intervention of these disabilities using mobile applications in Sri Lanka. In this research, one of the first deep learning and machine learning based mobile applications, named “Pubudu” was developed for screening and intervention of dyslexia, dysgraphia and dyscalculia supporting local languages. In “Pubudu” we have followed up clinical screening and diagnostic procedures recommended by health professionals for screening and intervention. The screening of dyslexia, letter dysgraphia and numeric dysgraphia was carried out using deep neural network and the screening for dyscalculia was carried out using machine learning techniques. Intervention techniques are implemented using gamified environments. System testing was carried out using 50 differently abled children and 50 typical children. With the initial dataset 88%, 58%, 99% screening accuracies are achieved in neural networks for letter dysgraphia, dyslexia and numeric dysgraphia screening while dysgraphia, whereas 90% accuracy was achieved for dyscalculia. Handwritten letters and numbers were fed as inputs to CNN model in letter dysgraphia and numeric dysgraphia while embedded audio clips of letter pronunciation were fed in to voice recognition CNN model in dyslexia. “Pubudu” shows significant potential for screening and intervention of dyslexia, dysgraphia and dyscalculia in local languages motivating children and interactively making them able and would be an enabling app for most of the underprivileged children in Sri Lanka.

A Gamified Approach for Screening and Intervention of Dyslexia, Dysgraphia and Dyscalculia

Abstract: This paper aims to diagnose children with specific learning disabilities and provide treatments via a mobile game. Learning disabilities are neurological disorders that affect the brain. Children with learning disabilities have trouble with learning compared to their fellow peers and quite often fall back academically since a majority of them go undiagnosed. The specific learning disabilities for which this paper provides screening are dyslexia a reading disability, dyscalculia a mathematical disability, letter dysgraphia and numeric dysgraphia are both writing disabilities. Deep learning and machine learning techniques are used in the screening process of these specific learning disabilities. Trained convolutional neural networks are used to detect the spoken letter/word, detect the written letter/word and detect the written number on the mobile application. Outputs from the convolutional neural network are fed into the models used for screening learning disabilities. The machine learning algorithms used in building the models include k-nearest neighbors, random forest and support vector machine. Screening results from the models built in this research provided an accuracy of 89%, 90%, 92%, 92% for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia respectively. This is the first game based screening and intervention tool for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia.

Considering emotions and contextual factors in music recommendation: a systematic literature review

Abstract: In recent years, several music recommendation systems have been developed with the aim of incorporating valuable information into the user’s modeling and recommendation process. The inclusion of emotions and contextual information in music recommendation applications is increasingly becoming a relevant aspect to improve the listening experience. Thus, the main aim of this systematic literature review (SLR) is investigating the music recommendation approaches that considers emotions and/or context (research question 1) as well as to identify the main gaps and challenges that still remain and need to be addressed by future research (research question 2). After an extensive research, 64 publications were identified to answer the research questions. The studies were analyzed and evaluated for relevance. The main approaches that consider emotions and context were identified. The results of the review indicate that most studies in the field that combine multiple approach related to emotions or context factors have improved the user’s hearing experience. The main contributions of this review are a set of aspects that we consider important to be addressed by the music recommendation systems, such as: user activity, satisfaction, feedback, cold-start problems, cognitive load, learning, personality, and user preference. In addition, we also present a broad discussion about the challenges, difficulties and limitations that exist in music recommendation systems that consider emotions and contextual factors.